Overview

Dataset statistics

Number of variables12
Number of observations8771
Missing cells3355
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory890.8 KiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric11

Warnings

Dwutlenek_azotu is highly correlated with Tlenki_azotuHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotuHigh correlation
PM10 is highly correlated with PM_2_5 and 1 other fieldsHigh correlation
PM_2_5 is highly correlated with PM10 and 1 other fieldsHigh correlation
Benzen is highly correlated with PM10 and 1 other fieldsHigh correlation
Dwutlenek_azotu is highly correlated with Tlenki_azotu and 1 other fieldsHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotu and 1 other fieldsHigh correlation
PM10 is highly correlated with PM_2_5 and 2 other fieldsHigh correlation
PM_2_5 is highly correlated with PM10 and 2 other fieldsHigh correlation
Benzen is highly correlated with PM10 and 3 other fieldsHigh correlation
Tlenek_wegla is highly correlated with Dwutlenek_azotu and 4 other fieldsHigh correlation
Temperatura is highly correlated with BenzenHigh correlation
Dwutlenek_azotu is highly correlated with Tlenki_azotuHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotu and 1 other fieldsHigh correlation
PM10 is highly correlated with PM_2_5 and 1 other fieldsHigh correlation
PM_2_5 is highly correlated with PM10 and 1 other fieldsHigh correlation
Benzen is highly correlated with PM10 and 2 other fieldsHigh correlation
Tlenek_wegla is highly correlated with Tlenki_azotu and 1 other fieldsHigh correlation
PM10 is highly correlated with Temperatura and 2 other fieldsHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotuHigh correlation
Temperatura is highly correlated with PM10 and 2 other fieldsHigh correlation
Wilgotnosc is highly correlated with TemperaturaHigh correlation
PM_2_5 is highly correlated with PM10 and 2 other fieldsHigh correlation
Benzen is highly correlated with PM10 and 1 other fieldsHigh correlation
Dwutlenek_azotu is highly correlated with Tlenki_azotuHigh correlation
Kierunek_wiatru has 671 (7.7%) missing values Missing
Predkosc_wiatru has 671 (7.7%) missing values Missing
Temperatura has 671 (7.7%) missing values Missing
Wilgotnosc has 671 (7.7%) missing values Missing
Cisnienie has 671 (7.7%) missing values Missing
Tlenek_wegla is highly skewed (γ1 = 22.82293213) Skewed
Data has unique values Unique

Reproduction

Analysis started2021-12-13 19:51:45.781055
Analysis finished2021-12-13 19:52:19.687673
Duration33.91 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Data
Date

UNIQUE

Distinct8771
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size137.0 KiB
Minimum2020-01-01 01:00:00
Maximum2020-12-31 23:00:00
2021-12-13T20:52:19.896117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:20.136472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Dwutlenek_azotu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1100
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.77967808
Minimum4.1
Maximum170.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:20.397776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile18
Q133.4
median47.2
Q361.65
95-th percentile85.95
Maximum170.1
Range166
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation21.00320128
Coefficient of variation (CV)0.4305727735
Kurtosis0.5045366901
Mean48.77967808
Median Absolute Deviation (MAD)14.1
Skewness0.5906604039
Sum427846.5564
Variance441.1344639
MonotonicityNot monotonic
2021-12-13T20:52:20.619181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.530
 
0.3%
53.927
 
0.3%
57.527
 
0.3%
28.725
 
0.3%
50.225
 
0.3%
32.625
 
0.3%
41.424
 
0.3%
41.924
 
0.3%
40.524
 
0.3%
47.623
 
0.3%
Other values (1090)8517
97.1%
ValueCountFrequency (%)
4.11
< 0.1%
4.21
< 0.1%
4.81
< 0.1%
5.31
< 0.1%
5.41
< 0.1%
5.71
< 0.1%
61
< 0.1%
6.11
< 0.1%
6.22
< 0.1%
6.31
< 0.1%
ValueCountFrequency (%)
170.11
< 0.1%
150.71
< 0.1%
143.81
< 0.1%
139.31
< 0.1%
138.42
< 0.1%
134.91
< 0.1%
134.61
< 0.1%
130.11
< 0.1%
128.51
< 0.1%
127.51
< 0.1%

Tlenki_azotu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3204
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.9480854
Minimum0.1
Maximum931.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:20.858541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile30.55
Q173.2
median120.1
Q3188.3
95-th percentile353.05
Maximum931.5
Range931.4
Interquartile range (IQR)115.1

Descriptive statistics

Standard deviation102.5131055
Coefficient of variation (CV)0.7072401487
Kurtosis3.662742595
Mean144.9480854
Median Absolute Deviation (MAD)54.4
Skewness1.598405934
Sum1271339.657
Variance10508.9368
MonotonicityNot monotonic
2021-12-13T20:52:21.099880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.712
 
0.1%
52.611
 
0.1%
75.511
 
0.1%
84.511
 
0.1%
0.111
 
0.1%
35.410
 
0.1%
84.910
 
0.1%
76.210
 
0.1%
74.710
 
0.1%
126.910
 
0.1%
Other values (3194)8665
98.8%
ValueCountFrequency (%)
0.111
0.1%
0.29
0.1%
0.31
 
< 0.1%
0.42
 
< 0.1%
0.52
 
< 0.1%
0.63
 
< 0.1%
0.72
 
< 0.1%
0.81
 
< 0.1%
0.93
 
< 0.1%
13
 
< 0.1%
ValueCountFrequency (%)
931.51
< 0.1%
858.71
< 0.1%
806.71
< 0.1%
721.51
< 0.1%
705.71
< 0.1%
6961
< 0.1%
688.21
< 0.1%
677.61
< 0.1%
677.31
< 0.1%
668.21
< 0.1%

PM10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1523
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.10540962
Minimum3
Maximum248.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:21.343229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11.7
Q121.17168675
median30.8
Q349.1
95-th percentile93.65
Maximum248.3
Range245.3
Interquartile range (IQR)27.92831325

Descriptive statistics

Standard deviation26.94373722
Coefficient of variation (CV)0.6890028126
Kurtosis4.819160262
Mean39.10540962
Median Absolute Deviation (MAD)12.1
Skewness1.849077004
Sum342993.5478
Variance725.9649751
MonotonicityNot monotonic
2021-12-13T20:52:21.604531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.7380281738
 
0.4%
25.937
 
0.4%
2333
 
0.4%
23.831
 
0.4%
25.831
 
0.4%
20.931
 
0.4%
24.530
 
0.3%
14.530
 
0.3%
21.730
 
0.3%
1829
 
0.3%
Other values (1513)8451
96.4%
ValueCountFrequency (%)
39
0.1%
3.81
 
< 0.1%
3.98
0.1%
41
 
< 0.1%
4.31
 
< 0.1%
4.63
 
< 0.1%
4.71
 
< 0.1%
4.86
0.1%
4.91
 
< 0.1%
5.21
 
< 0.1%
ValueCountFrequency (%)
248.31
< 0.1%
227.81
< 0.1%
225.71
< 0.1%
2141
< 0.1%
211.51
< 0.1%
207.91
< 0.1%
207.71
< 0.1%
202.41
< 0.1%
201.81
< 0.1%
198.41
< 0.1%

PM_2_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1019
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.09220515
Minimum0
Maximum226.1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:21.850871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7
Q110.5
median17
Q328.9
95-th percentile62.35
Maximum226.1
Range226.1
Interquartile range (IQR)18.4

Descriptive statistics

Standard deviation19.94784889
Coefficient of variation (CV)0.8638347338
Kurtosis9.556195872
Mean23.09220515
Median Absolute Deviation (MAD)8
Skewness2.441328729
Sum202541.7314
Variance397.9166752
MonotonicityNot monotonic
2021-12-13T20:52:22.073278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.265
 
0.7%
0.348
 
0.5%
11.347
 
0.5%
11.847
 
0.5%
12.746
 
0.5%
15.246
 
0.5%
946
 
0.5%
9.146
 
0.5%
7.345
 
0.5%
7.844
 
0.5%
Other values (1009)8291
94.5%
ValueCountFrequency (%)
02
 
< 0.1%
0.131
0.4%
0.265
0.7%
0.348
0.5%
0.429
0.3%
0.519
 
0.2%
0.65
 
0.1%
0.72
 
< 0.1%
0.83
 
< 0.1%
0.95
 
0.1%
ValueCountFrequency (%)
226.11
< 0.1%
207.91
< 0.1%
204.91
< 0.1%
194.91
< 0.1%
180.51
< 0.1%
157.51
< 0.1%
157.11
< 0.1%
156.81
< 0.1%
155.31
< 0.1%
152.71
< 0.1%

Benzen
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct537
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.162216619
Minimum0
Maximum21.1
Zeros44
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:22.288711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.5
Q31.4
95-th percentile4.4
Maximum21.1
Range21.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.702871031
Coefficient of variation (CV)1.465192464
Kurtosis18.03612201
Mean1.162216619
Median Absolute Deviation (MAD)0.3
Skewness3.464810284
Sum10193.80196
Variance2.899769748
MonotonicityNot monotonic
2021-12-13T20:52:22.624541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21480
16.9%
0.1999
 
11.4%
0.3958
 
10.9%
0.4596
 
6.8%
0.5439
 
5.0%
0.6314
 
3.6%
0.7273
 
3.1%
0.8257
 
2.9%
0.9194
 
2.2%
1161
 
1.8%
Other values (527)3100
35.3%
ValueCountFrequency (%)
044
 
0.5%
0.1999
11.4%
0.18666666671
 
< 0.1%
0.1867469881
 
< 0.1%
0.1871951221
 
< 0.1%
0.18773006131
 
< 0.1%
0.18827160491
 
< 0.1%
0.18881987581
 
< 0.1%
0.191
 
< 0.1%
0.19119496861
 
< 0.1%
ValueCountFrequency (%)
21.11
< 0.1%
19.71
< 0.1%
19.11
< 0.1%
18.51
< 0.1%
14.81
< 0.1%
14.52
< 0.1%
14.41
< 0.1%
13.81
< 0.1%
13.72
< 0.1%
13.61
< 0.1%

Tlenek_wegla
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct136
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9198350956
Minimum0.2
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:22.827997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.5
median0.6
Q30.9
95-th percentile1.5
Maximum147
Range146.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation3.465951432
Coefficient of variation (CV)3.768013907
Kurtosis674.7476894
Mean0.9198350956
Median Absolute Deviation (MAD)0.2
Skewness22.82293213
Sum8067.873623
Variance12.01281933
MonotonicityNot monotonic
2021-12-13T20:52:23.049424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51506
17.2%
0.41352
15.4%
0.61298
14.8%
0.7975
11.1%
0.8710
8.1%
0.3579
 
6.6%
0.9534
 
6.1%
1391
 
4.5%
1.1272
 
3.1%
1.2229
 
2.6%
Other values (126)925
10.5%
ValueCountFrequency (%)
0.274
 
0.8%
0.3579
 
6.6%
0.41352
15.4%
0.51506
17.2%
0.52095808381
 
< 0.1%
0.59940119761
 
< 0.1%
0.61298
14.8%
0.60060240961
 
< 0.1%
0.60242424241
 
< 0.1%
0.60487804881
 
< 0.1%
ValueCountFrequency (%)
1471
< 0.1%
113.81
< 0.1%
96.81
< 0.1%
77.31
< 0.1%
64.81
< 0.1%
63.91
< 0.1%
63.81
< 0.1%
60.81
< 0.1%
52.41
< 0.1%
51.52
< 0.1%

Kierunek_wiatru
Real number (ℝ≥0)

MISSING

Distinct747
Distinct (%)9.2%
Missing671
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean216.1157322
Minimum0
Maximum360
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:23.275812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41
Q1168
median247
Q3279
95-th percentile330.05
Maximum360
Range360
Interquartile range (IQR)111

Descriptive statistics

Standard deviation91.57796727
Coefficient of variation (CV)0.4237450293
Kurtosis-0.4991715839
Mean216.1157322
Median Absolute Deviation (MAD)40
Skewness-0.822101508
Sum1750537.431
Variance8386.52409
MonotonicityNot monotonic
2021-12-13T20:52:23.549091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25284
 
1.0%
24584
 
1.0%
24678
 
0.9%
24776
 
0.9%
25175
 
0.9%
28075
 
0.9%
24474
 
0.8%
27973
 
0.8%
28270
 
0.8%
27770
 
0.8%
Other values (737)7341
83.7%
(Missing)671
 
7.7%
ValueCountFrequency (%)
06
 
0.1%
112
0.1%
210
0.1%
317
0.2%
410
0.1%
510
0.1%
616
0.2%
717
0.2%
86
 
0.1%
915
0.2%
ValueCountFrequency (%)
3604
 
< 0.1%
3597
 
0.1%
3589
0.1%
35712
0.1%
35621
0.2%
35510
0.1%
35414
0.2%
35313
0.1%
35215
0.2%
35113
0.1%

Predkosc_wiatru
Real number (ℝ≥0)

MISSING

Distinct2790
Distinct (%)34.4%
Missing671
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean1.685936233
Minimum0.2583333333
Maximum7.626666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:23.765502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2583333333
5-th percentile0.4616666667
Q10.8145833333
median1.49533443
Q32.257083333
95-th percentile3.725
Maximum7.626666667
Range7.368333333
Interquartile range (IQR)1.4425

Descriptive statistics

Standard deviation1.065978087
Coefficient of variation (CV)0.6322766343
Kurtosis1.66277869
Mean1.685936233
Median Absolute Deviation (MAD)0.7108333333
Skewness1.174406677
Sum13656.08349
Variance1.136309282
MonotonicityNot monotonic
2021-12-13T20:52:23.994889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54516
 
0.2%
0.703333333315
 
0.2%
0.641666666715
 
0.2%
0.663333333315
 
0.2%
0.551666666714
 
0.2%
0.521666666714
 
0.2%
0.563333333314
 
0.2%
0.511666666714
 
0.2%
0.608333333313
 
0.1%
0.626666666713
 
0.1%
Other values (2780)7957
90.7%
(Missing)671
 
7.7%
ValueCountFrequency (%)
0.25833333331
< 0.1%
0.26166666671
< 0.1%
0.26333333331
< 0.1%
0.27166666671
< 0.1%
0.27333333331
< 0.1%
0.27833333332
< 0.1%
0.281
< 0.1%
0.28333333331
< 0.1%
0.28793103451
< 0.1%
0.29310344831
< 0.1%
ValueCountFrequency (%)
7.6266666671
< 0.1%
7.4116666671
< 0.1%
6.9066666671
< 0.1%
6.9033333331
< 0.1%
6.6783333331
< 0.1%
6.6316666671
< 0.1%
6.621
< 0.1%
6.6083333331
< 0.1%
6.4433333331
< 0.1%
6.4283333331
< 0.1%

Temperatura
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6665
Distinct (%)82.3%
Missing671
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean10.48465701
Minimum-6.131666667
Maximum32.035
Zeros1
Zeros (%)< 0.1%
Negative634
Negative (%)7.2%
Memory size137.0 KiB
2021-12-13T20:52:24.210313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-6.131666667
5-th percentile-0.9918333333
Q14.034583333
median9.1975
Q317.14166667
95-th percentile24.46691667
Maximum32.035
Range38.16666667
Interquartile range (IQR)13.10708333

Descriptive statistics

Standard deviation7.988302995
Coefficient of variation (CV)0.7619040843
Kurtosis-0.7849717057
Mean10.48465701
Median Absolute Deviation (MAD)6.256666667
Skewness0.3441674075
Sum84925.72174
Variance63.81298474
MonotonicityNot monotonic
2021-12-13T20:52:24.425834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4466666675
 
0.1%
6.5183333335
 
0.1%
1.2254
 
< 0.1%
13.444
 
< 0.1%
3.1583333334
 
< 0.1%
0.49666666674
 
< 0.1%
4.3666666674
 
< 0.1%
7.0933333334
 
< 0.1%
0.0016666666674
 
< 0.1%
0.2854
 
< 0.1%
Other values (6655)8058
91.9%
(Missing)671
 
7.7%
ValueCountFrequency (%)
-6.1316666671
< 0.1%
-6.1033333331
< 0.1%
-5.9651
< 0.1%
-5.9066666671
< 0.1%
-5.7483333331
< 0.1%
-5.441
< 0.1%
-5.3683333331
< 0.1%
-5.0233333331
< 0.1%
-4.9266666671
< 0.1%
-4.8883333331
< 0.1%
ValueCountFrequency (%)
32.0351
< 0.1%
31.918333331
< 0.1%
31.838333331
< 0.1%
31.816666671
< 0.1%
31.771666671
< 0.1%
31.71
< 0.1%
31.681
< 0.1%
31.448333331
< 0.1%
31.383333331
< 0.1%
31.346666671
< 0.1%

Wilgotnosc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7234
Distinct (%)89.3%
Missing671
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean60.71809323
Minimum8.856666667
Maximum85.37833333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:24.654115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum8.856666667
5-th percentile29.30825
Q149.455
median63.61166667
Q374.84541667
95-th percentile81.91833333
Maximum85.37833333
Range76.52166667
Interquartile range (IQR)25.39041667

Descriptive statistics

Standard deviation16.80077742
Coefficient of variation (CV)0.2767013343
Kurtosis-0.56505911
Mean60.71809323
Median Absolute Deviation (MAD)12.09666667
Skewness-0.6169751364
Sum491816.5552
Variance282.2661218
MonotonicityNot monotonic
2021-12-13T20:52:25.167690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.994
 
< 0.1%
58.364
 
< 0.1%
76.068333334
 
< 0.1%
75.338333334
 
< 0.1%
82.124
 
< 0.1%
78.243333334
 
< 0.1%
73.176666674
 
< 0.1%
73.041666673
 
< 0.1%
69.263
 
< 0.1%
69.3653
 
< 0.1%
Other values (7224)8063
91.9%
(Missing)671
 
7.7%
ValueCountFrequency (%)
8.8566666671
< 0.1%
9.371
< 0.1%
9.4451
< 0.1%
10.0951
< 0.1%
10.181666671
< 0.1%
10.271666671
< 0.1%
10.296666671
< 0.1%
10.61
< 0.1%
10.873333331
< 0.1%
10.931
< 0.1%
ValueCountFrequency (%)
85.378333331
< 0.1%
85.361666671
< 0.1%
85.358333331
< 0.1%
85.348333331
< 0.1%
85.333333331
< 0.1%
85.31
< 0.1%
85.291666672
< 0.1%
85.288333331
< 0.1%
85.266666671
< 0.1%
85.248333331
< 0.1%

Cisnienie
Real number (ℝ≥0)

MISSING

Distinct6671
Distinct (%)82.4%
Missing671
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean990.0361387
Minimum958.8811321
Maximum1017.685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2021-12-13T20:52:25.401066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum958.8811321
5-th percentile976.7115833
Q1984.4241667
median990.0233333
Q3995.6129167
95-th percentile1003.235
Maximum1017.685
Range58.80386792
Interquartile range (IQR)11.18875

Descriptive statistics

Standard deviation8.095617506
Coefficient of variation (CV)0.008177092926
Kurtosis0.2140026528
Mean990.0361387
Median Absolute Deviation (MAD)5.594166667
Skewness-0.06796337826
Sum8019292.723
Variance65.53902281
MonotonicityNot monotonic
2021-12-13T20:52:25.614505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
995.5056
 
0.1%
989.9155
 
0.1%
991.2955
 
0.1%
987.34166675
 
0.1%
984.1855
 
0.1%
994.13166674
 
< 0.1%
983.874
 
< 0.1%
996.95666674
 
< 0.1%
998.64
 
< 0.1%
995.4954
 
< 0.1%
Other values (6661)8054
91.8%
(Missing)671
 
7.7%
ValueCountFrequency (%)
958.88113211
< 0.1%
959.16603771
< 0.1%
959.55849061
< 0.1%
959.85283021
< 0.1%
960.27358491
< 0.1%
960.81698111
< 0.1%
961.11886791
< 0.1%
961.98301891
< 0.1%
962.8452831
< 0.1%
963.45849061
< 0.1%
ValueCountFrequency (%)
1017.6851
< 0.1%
1017.621
< 0.1%
1017.6016671
< 0.1%
1017.5283331
< 0.1%
1017.5183331
< 0.1%
1017.5116671
< 0.1%
1017.4533331
< 0.1%
1017.4283331
< 0.1%
1017.4051
< 0.1%
1017.331
< 0.1%

Interactions

2021-12-13T20:51:50.580597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:50.856858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:51.044360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:51.245819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:51.440126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:51.641588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:51.857013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:52.062460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:52.277912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:52.482338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:52.683799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:52.900251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:53.205404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:53.376946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:53.548181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:53.724894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:53.900457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:54.079943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:54.295366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:54.511788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:54.701194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:54.872734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:55.118078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:55.322531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:55.513020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:55.693537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:55.920928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:56.107394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:56.294691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:56.591896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:56.777400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:56.969885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:57.157384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:57.372755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:57.575210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:57.732791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:57.902337image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:58.075871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:58.249406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:58.501645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:58.918530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:59.105033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:59.305495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:59.517925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:59.703428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:51:59.900900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:00.083412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:00.268917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:00.450431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:00.679818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:00.867328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:01.055825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:01.234353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:01.555790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:01.815095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:02.003592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:02.208045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:02.458376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:02.653854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:02.864289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:03.057773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:03.284168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:03.505573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:03.751139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:04.016428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:04.225868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:04.462237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:04.706583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:04.901065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:05.101530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:05.288026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:05.482506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:05.741812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:05.992144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:06.402046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:06.639409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:06.849850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:07.046323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:07.291665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:07.485150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:07.682622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:07.878096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:08.070585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:08.345845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:08.642114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:08.874692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:09.198826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:09.509990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:09.739377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:09.992699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:10.225959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:10.417446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:10.615919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:10.807406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:11.079484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:11.341237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:11.555663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:11.747151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:12.016790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:12.243135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:12.627007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:13.098743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:13.343092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:13.564498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:13.752980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:13.961454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:14.184826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:14.385559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:14.767507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:15.100716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:15.426843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:15.764939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:16.090067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:16.411493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:16.863283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:17.044799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:17.256235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:17.590339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:17.819676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:18.023133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-13T20:52:18.239553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-12-13T20:52:25.825929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-13T20:52:26.153152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-13T20:52:26.605942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-13T20:52:26.983930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-13T20:52:18.622540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-13T20:52:19.031438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-13T20:52:19.332624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-13T20:52:19.525108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DataDwutlenek_azotuTlenki_azotuPM10PM_2_5BenzenTlenek_weglaKierunek_wiatruPredkosc_wiatruTemperaturaWilgotnoscCisnienie
02020-01-01 01:00:0019.735.034.317.90.20.7278.02.4716674.40333362.0583331002.798333
12020-01-01 02:00:0022.843.532.215.00.20.7279.02.2000004.11333361.3366671002.940000
22020-01-01 03:00:0031.868.435.716.30.20.8270.01.9066673.83000062.4100001003.261667
32020-01-01 04:00:0026.452.634.015.00.20.7278.01.9983333.48000063.5416671003.481667
42020-01-01 05:00:0024.550.426.111.60.20.7277.02.2483333.13500064.7866671003.888333
52020-01-01 06:00:0022.950.125.911.10.10.7276.01.7883332.81833365.6366671004.068333
62020-01-01 07:00:0021.943.422.610.60.20.7275.02.0883332.78500065.1666671004.423333
72020-01-01 08:00:0027.851.418.69.50.10.7271.02.2766673.34666760.1550001004.938333
82020-01-01 09:00:0024.643.220.310.10.20.6268.02.6100003.46333358.8500001004.996667
92020-01-01 10:00:0022.940.129.210.60.10.7277.02.6516673.54333359.3916671005.078333

Last rows

DataDwutlenek_azotuTlenki_azotuPM10PM_2_5BenzenTlenek_weglaKierunek_wiatruPredkosc_wiatruTemperaturaWilgotnoscCisnienie
87612020-12-31 14:00:0047.9171.449.020.41.71.0196.6148651.7126283.65659663.920163978.943821
87622020-12-31 15:00:0051.0169.652.321.71.61.0196.1972791.7124993.62785763.890668978.947958
87632020-12-31 16:00:0055.8188.051.824.52.51.1195.8972601.7009413.59969763.888388978.953121
87642020-12-31 17:00:0053.0166.349.725.92.21.1195.5655171.6904073.57287363.884577978.958953
87652020-12-31 18:00:0051.1142.549.227.22.61.0194.9305561.6755373.55036463.863221978.964360
87662020-12-31 19:00:0044.8162.252.328.92.41.1194.3216781.6665203.53324963.819346978.969874
87672020-12-31 20:00:0039.5108.152.630.12.70.9193.8380281.6630333.51540363.779274978.975156
87682020-12-31 21:00:0039.383.747.827.31.80.8193.2695041.6601943.49047463.759520978.979963
87692020-12-31 22:00:0041.3106.352.531.21.80.9192.5357141.6589813.46468463.741620978.985021
87702020-12-31 23:00:0039.391.266.741.93.41.1191.8201441.6590933.45369863.682152978.984014